The tracking of morning fatigue status across in-season trainingweeks in elite soccer players

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Authors Thorpe, R. T. (Robin); Strudwick, A. J. (Anthony);Buchheit, M. (Martin); Atkinson, G. (Greg); Drust, B.(Barry); Gregson, W. (Warren)

Citation Thorpe, R. T., Strudwick, A. J., Buchheit, M., Atkinson,G., Drust, B., Gregson, W. (2016) 'The tracking of morningfatigue status across in-season training weeks in elitesoccer players' International Journal of Sports Physiologyand Performance; 11 ( 7 ): pp. 947 - 952

Eprint Version Author accepted manuscript

DOI 10.1123/ijspp.2015-0490

Publisher Human Kinetics

Journal International Journal of Sports Physiology andPerformance

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1

The tracking of morning fatigue status across in-season training weeks in elite soccer 1 players 2

RT. Thorpe1,2, AJ. Strudwick1,2, M. Buchheit,4,5, G. Atkinson3, B. Drust2, W. Gregson2 3

1 Football Medicine and Science Department, Manchester United Football Club, UK 4

2Football Exchange, Research Institute for Sport and Exercise Sciences, Liverpool John Moores 5

University, UK 6

3 Health and Social Care Institute, Teesside University, UK 7

4 Sport Science Department, Myorobie Association, Montvalezan, France 8

5 Performance Department, Paris Saint Germain FC 9

10

Corresponding Author: 11

Robin T. Thorpe 12

Manchester United, Aon Training Complex, Birch Road off Isherwood Road, Carrington, Manchester 13

M31 4BH 14

E-mail: robin.thorpe@manutd.co.uk 15

Key Words-Training, Performance, Wellness, Recovery 16

17

Word count: 2989 18

Abstract word count: 250 19

Number of figures: 1 20

Number of tables: 1 21

22

2

The tracking of morning fatigue status across in-season training weeks in elite soccer 23 players 24

RT. Thorpe1, AJ Strudwick1, M. Buchheit3,4, G. Atkinson2, B. Drust1, W. Gregson1 25

1 Football Exchange, Research Institute for Sport and Exercise Sciences, Liverpool John 26 Moores University, UK 27 2 Health and Social Care Institute, Teesside University, UK 28 3 Sport Science Department, Myorobie Association, Montvalezan, France 29 4 Performance Department, Paris Saint Germain FC 30

Abstract 31

Purpose To quantify the mean daily changes in training and match load and any parallel 32 changes in indicators of morning-measured fatigue across in-season training weeks in elite 33 soccer players. 34

Methods Following each training session and match, ratings of perceived exertion (s-RPE) 35 were recorded to calculate overall session load (RPE-TL) in 29 English Premier League 36 players from the same team. Morning ratings of fatigue, sleep quality, delayed-onset muscle 37 soreness (DOMS), as well as sub-maximal exercise heart rate (HRex), post-exercise heart rate 38 recovery (HRR%) and variability (HRV) were also recorded pre-match day and one, two and 39 four days post-match. Data were collected for a median duration of 3 weeks (range:1-13) and 40 reduced to a typical weekly cycle including no mid-week match and a weekend match day. 41

Data were analysed using within-subjects linear mixed models. 42

Results 43

RPE-TL was approximately 600 AU (95%CI: 546-644) higher on match-day vs the following 44 day (P<0.001). RPE-TL progressively decreased by ≈ 60 AU per day over the 3 days prior to 45 a match (P<0.05). Morning-measured fatigue, sleep quality and DOMS tracked the changes 46 in RPE-TL, being 35-40% worse on post-match day vs pre-match day (P<0.001). Perceived 47 fatigue, sleep quality and DOMS improved by 17-26% from post-match day to three days 48 post-match with further smaller (7-14%) improvements occurring between four days post-49

match and pre-match day (P<0.01). There were no substantial or statistically significant 50

changes in HRex, HRR% and HRV over the weekly cycle (P>0.05). 51

Conclusions Morning-measured ratings of fatigue, sleep quality and DOMS are clearly more 52 sensitive than HR-derived indices to the daily fluctuations in session load experienced by 53 elite soccer players within a standard in-season week. 54

55

3

Introduction 56

It is important to allow sufficient recovery between training sessions and competitions. An 57 imbalance between training/competition load and recovery may, over extended periods of 58 time contribute to potentially long-term debilitating effects associated with overtraining. 1

59 Consequently, attention is increasingly being given to the evaluation of monitoring tools 60 which may indicate the general fatigue status of athletes. These indicators include heart rate 61 derived indices, 2 salivary hormones, neuromuscular indices 3 and perceived wellness ratings. 62 4–6 63

A valid marker of fatigue should be sensitive to variability in training load. 7 Researchers 64 have therefore examined the sensitivity of potential measures of fatigue to daily fluctuations 65 in training load in elite team sport athletes. 4–6 For example, in both elite Australian Rules 66

Football 4 and elite soccer 6 players, small to large and statistically significant correlations 67 were reported between fluctuations in daily training load and changes in both perceived 68 ratings of wellness and vagal-related heart rate variability indices. These findings suggest that 69 such measures show particular promise as acute, simple, non-invasive assessments of fatigue 70

status in elite team sport athletes. 71

Further evaluation of the validity of potential fatigue measures can be undertaken by 72 examining their sensitivity to prescribed changes in training load over extended periods of 73 time. Whilst these relationships have been examined in individual endurance based sports, 8,9 74 limited attention has been given to elite team sport athletes, 5 who are required to compete 75 weekly and often bi-weekly across the competition period. In these athletes, a key component 76

of the in-season and within-week training prescription resides around the need to periodise 77 the training load in order to minimise player fatigue ahead of the weekly matches. 10 Gastin 78 and colleagues (2013) 5 recently reported that subjective ratings of physical and 79 psychological wellness (fatigue, muscle strain, hamstring strain, quadriceps strain, 80 pain/stiffness, power, sleep quality, stress and wellbeing) were sensitive to within-week 81 training manipulations (i.e. improved steadily throughout the week to a game day low) in 82 elite Australian Football players. However, to the best of our knowledge, no researcher has 83 examined the sensitivity of simple, non-invasive potential measures of fatigue across in-84 season training weeks in elite soccer players. Since differences exist in the physiological 85

demands between team sports it is important to determine which potential fatigue variables 86 are sensitive to changes in load associated with specific sports. Therefore, our aim was to 87 quantify any changes in perceived ratings of wellness and objective measures of vagal-related 88

heart rate indices that occur across standard in-season training weeks in elite soccer players. 89

90

Methods 91

Subjects 92

Twenty-nine soccer players (age 27 ± 5.1 years; height 181 ± 7.1 cm; 78 weight ± 6.1 kg) 93

from the same team competing in the English Premier League participated in this study. 94

Design 95

Player training load was assessed on six days; on the pre-match day, match-day and one, two, 96 four and five days after the match across standard training weeks (no mid-week match; 97

median of 3 weeks per player; range = 1-13) during the 2012/2013 in-season competitive 98

period (August to May). Players were required to complete a minimum match duration of 60-99

4

min in order for their weekly data to be included in the present study. Players did not train 100 and were given a day off three days after a match. Players took part in normal team training 101 throughout the period as prescribed by the coaching staff. Players performed a range of 102 recovery interventions the day following the match including low-intensity cycling, foam 103 rolling and hydrotherapy. All players were fully familiarised with the assessments in the 104

weeks prior to completion of the main experimental trials. 105

Fatigue measures were assessed on the day prior to the match and one, two and four days 106 following the match. On the day of the fatigue assessments (perceived ratings of wellness, 107 sub-maximal heart rate, heart rate recovery and heart rate variability), players arrived at the 108 training ground laboratory having refrained from caffeine and alcohol intake at least 12-hours 109 prior to each assessment point. Fatigue measures were subsequently taken prior to the players 110

commencing normal training. All trials were conducted at the same time of the day in order 111 to avoid the circadian variation in body temperature. 11 Players were not allowed to consume 112 fluid at any time during the fatigue assessments. The study was approved by Liverpool John 113 Moores University Ethics Committee. All players provided written informed consent. Prior to 114 inclusion into the study, players were examined by the club physician and were deemed to be 115

free from illness and injury. 116

Methods 117

Training Load Assessment Individual player daily training load was monitored throughout the 118 assessment period. Load (RPE-TL, arbitrary units, AU) was estimated for all players by 119 multiplying total training or match session duration (min) with session ratings of perceived 120

exertion (RPE). 12 Despite several influences, the usefulness of RPE in elite soccer has 121 previously been observed. 13 Player RPE was collected within 20-30-min following cessation 122

of the training session/match. 13 123

Perceived ratings of wellness A psychometric questionnaire was used to assess general 124 indicators of player wellness. 6,14 The questionnaire was comprised of three questions relating 125 to perceived overall fatigue, sleep quality and delayed-onset muscle soreness (DOMS) 6,14 126 Each question was scored on a seven-point scale [scores of 1-7 with 1 and 7 representing 127 very, very poor (negative state of wellness) and very, very good (positive state of wellness) 128

respectively]. Coefficients of variation for the three indices ranged from 9-13 %. 6 129

Sub-maximal exercise heart rate (HRex), post-exercise heart rate recovery (HRR) and heart 130 rate variability (HRV) Players completed an indoor submaximal 5-min cycling /5-min 131

recovery test (Keiser, California, USA) prior to commencing every session. 2,6 All players 132 were tested together at a fixed exercise intensity of 130 watts (85 rpm). The present intensity 133

was selected in order to minimize anaerobic energy contribution and to permit a rapid return 134 of heart rate to baseline for short-term heart rate (HR) variability (HRV) measurements. 15 On 135

completion of exercise, the players remained seated in silence for 5-min. 136

Sub-maximal HRex was calculated using the average of the final 30-sec of the cycle test. 16 137 HRR expressed as the absolute (HRR), and relative (%HRR) change in HR between the final 138 30-sec (average) of the 5-min cycling test and 60 sec after cessation of exercise were 139 calculated as previously described. 15,17 The coefficients of variation for HRex, HRR and 140 %HRR were 3%, 14% and 10% respectively. 6 HRV was measured during the recovery 141

period and expressed as the natural logarithm of the square root of the mean of the sum of 142

squares of differences between adjacent normal R-R intervals (Ln rMSSD) as previously 143 described 15 using Polar software (Polar Precision Performance SW 5.20, Polar Electro, 144 Kemple, Finland). Ln rMSSD has previously been shown to have greater reliability and 145

5

validity than spectral indices of HRV over short assessment periods. 18,19The coefficient of 146

variation for LnrMSSD was 10 % respectively. 6 147

148

Statistical Analyses 149

It was assumed that if an indicator of fatigue was not, at the very least, sensitive to 150 differences between the different loads on pre-match day and the post-match day, it cannot be 151 considered useful. Therefore, for the purpose of our sample size estimation, our primary 152 comparison was between the pre-match and post-match days. In a previous study, coefficients 153 of variation of approximately 10% have been reported for the indicators of fatigue we 154 studied. 6 Using this information of within-subjects variability, we estimated that a sample 155

size of 29 would allow the detection of a difference in fatigue between pre- and post-match 156

days of approximately 9% (two-tailed paired t-test, 90% statistical power, p<0.05). 157

A within-subjects linear mixed model was used to quantify mean differences between days 158 along with the respective 95% confidence intervals. It is difficult to ascertain the exact 159 relative influence of each study outcome on the actual performance of a soccer team, e.g. the 160 standard effect size of a particular outcome may be high in response to training or an 161 intervention, but the relative influence of this outcome on actual soccer performance may be 162 low, 20 Nevertheless, standardised effect sizes, estimated from the ratio of the mean 163 difference to the pooled standard deviation, were also calculated for each study outcome and 164 interpreted in the discussion section. Effect size (ES) values of 0.2, 0.5 and 0.8 were 165 considered to represent small, moderate and large differences, respectively 21. When the 166

model residuals were skewed or heteroscedastic, data were log-transformed and re-analysed. 167 We adopted the least significant difference approach to multiple comparisons in line with the 168

advice in 22,23 169

Results 170

Training load: The RPE-TL was greatest on match-day (≈ 600 AU). The peak-trough 171 difference in RPE-TL was approximately 550 AU (95% CI 546-644 AU) between match-day 172 and the following day (p<0.001). The RPE-TL progressively decreased by ≈ 60 AU per day 173

over the 3 days prior to a match (p<0.05) (Figure 1). 174

Perceived ratings of wellness: All the wellness outcomes showed a 35-40% worsening on the 175

post-match day vs the pre-match day. The 95%CIs for these changes were 1.2-1.6 AU, 1.0-176 1.5 AU and 1.1-1.5 AU for perceived fatigue, sleep quality and DOMS, respectively 177 (p<0.001). Wellness outcomes then improved by 17-26% between post-match day and two 178

days post-match. The 95%Cis for these changes were 0.7-1.1 AU, 0.7-1.2 AU and 0.4-0.9 179 AU for perceived fatigue, sleep quality, and DOMS. Wellness ratings then remained 180 relatively stable between the second and fourth day post-match. Further smaller (7-14%) 181 improvements occurred between the fourth day post-match and pre-match day (p<0.01). The 182 95% Cis for these changes were 0.2-0.6 AU, 0.1-0.6 AU and 0.4-0.7 AU for perceived 183

fatigue, sleep quality and DOMS (Table 1). 184

Heart rate indices: There were no substantial or statistically significant changes in HRex, 185

HRR% and HRV over all the weekdays (p>0.05) (Table 1). 186

187

Discussion 188

6

The aim of the present study was to quantify the mean daily changes in training load and 189 parallel changes in potential fatigue measures across in-season training weeks in elite soccer 190 players. The main finding was that perceived ratings of wellness but not HR-derived indices 191 are sensitive to the fluctuations in training load experienced by elite soccer players across in-192

season training weeks which involve only one match per week (no mid-week match). 193

Elite soccer players are required to compete on a weekly and often bi-weekly (mid-week 194 game) basis with additional training administered in-between matches. Training load 195 prescribed by coaches should therefore serve to ensure that fatigue is reduced on the days 196 when players are engaged in competition. In the present study, only training weeks 197 containing no mid-week game were used in order to examine changes in fatigue across a 198 ‘standard’ training week. A clear attempt to periodise training load across the week was 199

currently observed with the lowest load prescribed the day following a match with large (ES 200 >1.3) and statistically significant increases in training load prescribed two and four days 201 following the match. During the two subsequent days (fifth day post-match and pre-match 202 day) there was a moderate (ES=0.7) and statistically significant reduction in training load in 203 the lead into the next game (Figure 1). So far, little information currently exists with regards 204 to the patterns of training load undertaken by elite soccer players. 10,24 Interestingly, the 205 pattern of training load exhibited in the present study differs to that seen in recent 206 observations in Premier League players where only a reduction in daily training load was 207 observed one day prior to a match compared to the other training days. 10 However, Malone 208 and colleagues (2014) analysed all training weeks throughout the in-season competition 209 period including those containing a mid-week game. The combination of this and 210 dissimilarities in coaching philosophy and training methodology likely explain the difference 211

in training load periodization to the current study. Further research is warranted to explore the 212

patterns of training load experienced by elite players across different phases of the season. 213

Perceived ratings of wellness represent an increasingly popular method to assess athlete 214 fatigue. Recent work in both elite soccer 6 and Australian Rules Football 4 players 215 demonstrated that such ratings are sensitive to daily fluctuations in training load. Further 216 information concerning the validity of potential markers of fatigue can be derived by 217 examining their sensitivity to prescribed changes in training load over extended periods of 218 time. Whilst these relationships have been examined in individual endurance based sports 8,9 219 limited attempt to date has been made to determine the sensitivity of tools for monitoring 220

fatigue over extended periods of time in elite team sport players. 5 In the current study, the 221 between-day changes in perceived wellness across the weekly training cycle closely reflected 222

the prescribed distribution of training load. Moderate-to-large (ES 0.5-2.4) statistically 223 significant changes (35-40 %) in perceived ratings of fatigue, sleep quality and DOMS were 224

observed across the training week with the greatest and least amount of perceived fatigue, 225 sleep quality and DOMS reported on the day following and the day prior to a match 226 respectively (Table 1). These observations are consistent with findings in Australian Football 227

where perceived ratings of fatigue, muscle strain, hamstring strain, quadriceps strain, 228 pain/stiffness, power, sleep quality, stress and wellbeing improved by ~30% throughout the 229

week. 5 Interestingly, previous work in elite soccer examining the sensitivity of perceived 230 ratings of DOMS and sleep quality to acute daily fluctuations in training load failed to 231 observe any association. 6 However, any effect of training and match load on DOMS and 232

sleep quality, in particular the effects of match-play may materialise over a number of days 233 rather than immediately following the session.25 Collectively, previous observations 234

examining daily sensitivity in elite soccer 6 and Australian Rules players 4,5 combined with 235 the present findings suggest that attempts to fully examine the sensitivity of potential markers 236

7

of fatigue to changes in training and match load should be undertaken over both acute (daily) 237

and extended periods of time. 238

The increased perception of fatigue, poor sleep and DOMS currently observed following 239 match play is consistent with changes in biochemical status and reduced physical and 240 neuromuscular performance observed in the hours and days following soccer competition. 241 24,26–29 In contrast to many of the latter assessments, perceived wellness scales represent a 242 valid, time efficient and non-invasive means through which to derive information pertaining 243 to a players fatigue status. Such characteristics are important during the in-season competitive 244 phase, particularly during periods when players are required to compete in two or three 245 matches over a 7-day period where time constraints may restrict the use of more invasive 246 tests, and maximal performance tests may further debilitate the physical status of players 247

and/or increase the risk of injury. 30 In the present study, perceived ratings of fatigue and 248 DOMS remained similar over the second and fourth day post-match despite a rest day three 249 days after a match. This plateau may be due to the magnitude of the training load assigned 250 two days post-match (224 ± 166 AU) which provided sufficient stimulus to blunt a linear 251 improvement in player fatigue/recovery four days post-match and/or the fact that players 252 were relatively well recovered two days post-match (fatigue 4.6 AU; sleep quality 4.8 AU; 253 DOMS 4.3 AU). Interestingly, the progressive reduction in training load during the three days 254 leading into a match was accompanied by further moderate-to-large (ES 0.6-0.9) statistical 255 significant improvements in perceived wellness the day prior to a match (fatigue 5.0 AU; 256 sleep quality 5.1 AU; DOMS 5.2 AU) which suggests the players were still not fully 257 recovered four days post-match (Table 1). The time required to fully recovery following 258 match play has been shown to vary markedly (24-72hr) depending on the nature of the 259

physiological parameter assessed. 25 Furthermore, the rate of recovery is likely to be 260 influenced by a myriad of factors including the inherent variability in match demands 31 and 261 the athletes level of fitness 32. Alongside the changes in perceived ratings of fatigue and 262 DOMS, moderate-to-large (ES 0.6-1.4) statistical significant changes in ratings of sleep 263 quality were also observed across the week with the highest and lowest levels of sleep quality 264 observed during the evening of the fifth day post-match and the evening immediately 265 following a match respectively. These changes indicate the severe debilitating effects of the 266 match on perceived ratings of sleep quality. Indeed DOMS, inflammation, nervous system 267

activity and central excitation have all been reported as potential mechanisms of poor sleep 268 following competition. 33 Interestingly, data from elite endurance athletes have frequently 269 shown reductions in sleep quantity the night prior to competition. 34 Perceived ratings of 270 sleep were not measured the night prior to matches in the present study and therefore a 271

reduction may have occurred. Future work is required in order to further understand the 272

effects of training and match load on perceived and objective measures of sleep quality. 273

Heart rate (HR) indices (HRex, HRV and HRR) have recently been proposed as a non-274 invasive method to measure variations in the autonomic nervous system (ANS) in attempt to 275

understand athlete adaptation/fatigue status. 2 The use of vagal-related time domain indices 276 such as Ln rMSSD compared to spectral indices of HRV have been found to have greater 277

reliability and are ideal for assessments undertaken over shorter periods of time. 18,19 Recent 278 work in elite soccer players observed small (r=0.2), significant correlations between daily 279 fluctuations in training load and Ln rMSSD. 6 Similarly, in Australian Rules Football players 280

undergoing pre-season training, very large (r=0.80) and moderate (r=-0.40) significant 281 correlations were observed between daily training load and sub-maximal heart rate (HRex) 282

and a vagal related index of HRV (LnSD1). 4 In the present study, HRV (LnrMSSD), HRex 283 and HRR (%) remained unchanged across the training week (Table 1) despite the large 284 statistical significant fluctuations in training load (Figure 1). This suggests that in contrast to 285

8

perceived ratings of wellness, such indices lack the sensitivity to provide information 286 concerning changes in the fatigue status of elite soccer players across in-season training 287 weeks. It should be noted that the average daily training load in the current study (RPE-TL 288 228) is considerably lower than that reported during an AFL pre-season training camp (RPE-289 TL 746) where daily readings of HRex and HRV were negatively and positively associated 290 with load respectively. It is therefore possible that the magnitude of the fluctuations in daily 291 training load across the training week in the current study were insufficient to elicit changes 292 in HRex and HRV. 4 Alternatively, the shorter seven-day period over which observations 293 were made in the current study may not have been sufficient to detect any physiological 294 change. Indeed data derived from elite triathletes and adolescent Handball players suggests 295 the use of a single data point could be misleading for practitioners due to the high day-to-day 296 variation in these indices. 35,36 In elite triathletes when data were averaged over a week or 297

using a 7-day rolling average significant large correlations were found with 10-km running 298 performance compared to a single assessment point where negligible relationships were seen. 299 37. Additionally, changes in monthly HRV measurements were not sensitive to changes in 300 performance indices in young Handball players. 36 Future research is needed to establish 301

whether sensitivity of HR indices are seen over longer training periods in elite soccer players. 302

303

Conclusion 304

Perceived ratings of wellness are clearly more sensitive than HR-derived indices to the 305 within-week fluctuations in training load experienced by elite soccer players during typical 306

in-season training weeks. Therefore perceived ratings of wellness show particular promise as 307 simple, non-invasive assessments of fatigue status in elite soccer players throughout typical 308 in-season weeks in elite soccer players. 309

310

Acknowledgements 311

The authors would like to thank Manchester United Football Club for their cooperation and 312 input to this article 313

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Figure Caption 468

Figure 1. Training load (AU) across in-season training weeks (mean + SD) 469

† denotes sig. difference vs match-day. + denotes sig. difference vs pre-match day. * denotes 470

sig. difference vs two days post-match. # denotes sig. difference vs four days post-match.. 471

472

Table 1. Perceived ratings of fatigue, sleep quality and delayed-onset muscle soreness 473 (DOMS) (AU) and HRex (bpm), HRR (%) and Ln rMSSD (ms) across in-season training 474

weeks (mean + SD). 475

Day

Fatigue measure

Post-match day 2 days

post-match

4 days

post-match

Pre-match

day

Fatigue (AU) 3.4 ± 0.6 † 4.4 ± 0.7 † + 4.5 ± 0.7 † + 5.0 ± 0.6

Sleep quality (AU)

3.9 ± 1.2 † 4.8 ± 0.9 † + 4.7 ± 1.0 † 5.2 ± 0.8

DOMS (AU) 3.6 ± 0.6 † 4.3 ± 0.7 † + 4.4 ± 0.7 † + 5.1 ± 0.8

HRex (bpm) 119 ± 13 117 ± 14 119 ± 15 118 ± 13

HRR (%) 72.1 ± 7.7 71.5 ± 7.5 70.2 ± 7.7 70.9 ± 7.1

Ln rMSSD (ms) 3.31 ± 0.71

3.44 ± 0.69

3.28 ± 0.76

3.33 ± 0.64

476

† denotes sig. difference vs pre-match day. + denotes sig difference vs post-match day. 477

Scores of 1-7 with 1 and 7 representing very, very poor (negative state of wellness) and very, 478

very good (positive state of wellness respectively) 479

480